Authors

D.V. Sateesh Kaladhar Reddy

Assistant Professor, CSE Department, Sri Venkatesa Perumal College of Engineering & Technology, Andhra Pradesh, India

Dr. S. B. Sowjanya Kumar

Professor, CSE Department, Sri Venkatesa Perumal College of Engineering & Technology, Andhra Pradesh, India

S. Natesan

Associate Professor, CSE Department, Sri Venkatesa Perumal College of Engineering & Technology, Andhra Pradesh, India

B. Murali Prasad

Assistant Professor, CSE Department, Sri Venkatesa Perumal College of Engineering & Technology, Andhra Pradesh, India

Abstract

In today’s digital era, the escalating phenomenon of cyberbullying is a pervasive and growing concern. With the increasing prevalence of social media platforms, such as Twitter, online abusive behaviour has become a significant issue that often leads to unpleasant experiences for users. Manual detection of abnormal and bullying behaviour within the realm of social media is inherently not scalable [1]. Moreover, most existing studies on cyberbullying detection have been predominantly conducted in English and very limited work has been done on (a widely used language in Asia). This paper presents an approach for detecting cyberbullying in Roman Urdu tweets and identifying abuser profiles on Twitter. Firstly, we develop a text corpus of Roman Urdu tweets with user profile data. Subsequently, we employ Gated Recurrent Unit (GRU) model coupled with the application of word2vec technique for word embedding to develop a cyberbullying detection model [3]. Furthermore, we present temporal abusive tweet probability analysis method to provide announced analysis of the number of bullying and non-bullying tweets sent by individuals within a specific time interval. To evaluate the performance, we compare the GRU-based approach with other machine learning models.

Keywords

Gated Recurrent Unit Term Frequency Inverse Document Frequency (TF-IDF) Deep learning Cyberbullying XGBoost Boosting Classifier (GBM) and Support Vector Machine (SVM)

Citation of this Article

D.V. Sateesh Kaladhar Reddy, Dr. S. B. Sowjanya Kumar, S. Natesan, & B. Murali Prasad. (2026). An Explainable, Context-Aware AI Framework for Cyber-Aggression Detection and Behavioural Profiling in British English Social Media Discourse. International Current Journal of Engineering and Science (ICJES), 5(10), 1-7. Article DOI: https://doi.org/10.47001/ICJES/2026.510001 

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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